Sensitivity Analysis for Contagion Effects in Social Networks

Abstract

Analyses of social network data have suggested that obesity, smoking, happiness, and loneliness all travel through social networks. Individuals exert ‘‘contagion effects’’ on one another through social ties and association. These analyses have come under critique because of the possibility that homophily from unmeasured factors may explain these statistical associations and because similar findings can be obtained when the same methodology is applied to height, acne, and headaches, for which the conclusion of contagion effects seems somewhat less plausible. The author uses sensitivity analysis techniques to assess the extent to which supposed contagion effects for obesity, smoking, happiness, and loneliness might be explained away by homophily or confounding and the extent to which the critique using analysis of data on height, acne, and headaches is relevant. Sensitivity analyses suggest that contagion effects for obesity and smoking cessation are reasonably robust to possible latent homophily or environmental confounding; those for happiness and loneliness are somewhat less so. Supposed effects for height, acne, and headaches are all easily explained away by latent homophily and confounding. The methodology that has been used in past studies for contagion effects in social networks, when used in conjunction with sensitivity analysis, may prove useful in establishing social influence for various behaviors and states. The sensitivity analysis approach can be used to address the critique of latent homophily as a possible explanation of associations interpreted as contagion effects.